The genetics of personality

Nathan Gillespie & Nick Martin

Queensland Institute of Medical Research & University of Queensland,

Joint Genetics Program, Brisbane, Australia

Rephrasing an old question...

Free will versus determinism

Autonomy of the individual

Nature versus Nurture

A quantitative question...

Philosopher: “To what extent are behavioral, biomedical, and social outcomes biased by an individual’s DNA sequences?”

Psychologist: “To what extent are individual differences influenced by hereditary factors?”

Geneticist: “How much of the variance is due to genes and individual / family environment ?”

If genetic - empirical question...

Do monozygotic twins (clones) have identical outcomes ?

MZ twins reared apart - note the same way of supporting their cans of beer

MZ concordance for human conditions

Asthma 45%

Eczema 84%

Diabetes (type I) 56%

Schizophrenia 50%

Cleft lip/palate 30%

Club foot 23%

Homosexuality (M) 18%

Homosexuality (F) 23%

Is concordance due to genes (G) or shared-environment (C) ? Family studies - G + C confounded

MZ twins alone - G + C confounded

MZ twins reared apart

- Rare, atypical, selective placement?

Adoptions studies

- Rare, atypical, selective placement?

MZ and DZ twins reared together

Extended twin design

Twins typical of the population

- Prevalence of psychiatric symptoms - (Virginia 30 000)

- Slightly more middle class and better educated

- effect stronger in males (Australian Twin Registry)

Study design requirements- Appropriate sampling of MZ & DZs reared together

- Correct assignment of zygosity

- Comparison of means & variance in traits of interest in MZ & DZs to exclude zygosity environment

- Appropriate statistical methods to infer causes of variation

Model Building - Classical Twin Method

Study of MZ and DZ twins reared together in same home

- Most powerful method for detecting genetic & shared environmental effects:

P P =

A- (A) Additive genetic effects

aA

a

C

- (C) Shared environment effects

+ cC

c

D - (D) Non-additive genetic effects

(dominance or epistasis)

+ dD

d

E

- (E) Non-shared environmental effects

+ eE

e

Path Diagram - Classical twin study

P1

Twin1

P1

Twin2

c1

C1

c2

C2

e1 e2a1

A1 A2

a2

D1

d1 d2

D2 E2E1

1.0 MZ / DZ

1.0 MZ / 0.5 DZ

1.0 MZ / 0.25 DZ

Tracing rulesStandard rules for reading path diagrams to calculate the expected variance & covariance between variables / factors in the diagram

1.0 MZ / DZ

1.0 MZ / 0.5 DZ

P1

Twin1

e

E1 A1 C1

ac

P1

Twin2

c

C2 A2 E2

ae

(MZ) T1 T2

T1 a2+c2+e2 a2+c2

T2 a2+c2 a2+c2+e2

(DZ) T1 T2

T1 a2+c2+e2 .5a2+c2

T2 .5a2+c2 a2+c2+e21.0 MZ / DZ

1.0 MZ / 0.5 DZ

P1

Twin1

e1

E1 A1 C1

a1 c1

P1

Twin2

c2

C2 A2 E2

a1 e2

Expected variance / covariance matrices for MZ and DZ twin pairs under ACE model

Expected variance / covariance matrices for MZ and DZ twin pairs under AE model

(MZ) T1 T2

T1 a2 + e2 a2

T2 a2 a2 + e2

(DZ) T1 T2

T1 a2 + e2 .5a2

T2 .5a2 a2 + e2

1.0 MZ / 0.5 DZ

P1

Twin1

e1

E1 A1

a1

P1

Twin2

A2 E2

a1 e2

DZcorr = ½ MZcorr

Expected variance / covariance matrices for MZ and DZ twin pairs under DE model

(MZ) T1 T2

T1 d2 + e2 d 2

T2 d2 d2 + e2

(DZ) T1 T2

T1 d2 + e2 .25d2

T2 .25d2 d2 + e21.0MZ / .25DZ

P1

Twin1

e1

E1 D1

d1

P1

Twin2

d2

D2 E2

e2

DZcorr < ½ MZcorr

Expected variance / covariance matrices for MZ and DZ twin pairs under CE model

1.0 MZ / DZ

P1

Twin1

e1

E1 C1

c1

P1

Twin2

c2

C2 E2

e2DZcorr = MZcorr

(MZ) T1 T2

T1 c2 + e2 c 2

T2 c2 c2 + e2

(DZ) T1 T2

T1 c2 + e2 c2

T2 c2 c2 + e2

Expected variance / covariance matrices for MZ and DZ twin pairs under E model

(MZ) T1 T2

T1 e2 0

T2 0 e2

(DZ) T1 T2

T1 e2 0

T2 0 e2

P1

Twin1

e1

E1

P1

Twin2

E2

e2

DZcorr = MZcorr = 0

Maximum Likelihood (ML) Analysis

Data is typically summarized in a covariance matrix

Parameter estimates are found by minimising the "maximum likelihood function". This function can be expressed in several ways all of which are equivalent

FML ~= (S - Σ)'WML-1(S - Σ)

S = observed covariance matrix Σ = expected covariance matrixWML = weight matrix

ML analysis finds estimates of a, c, e & d which minimise the difference between the observed & expected covariance matrices

Neuroticism

r CI

MZFF .40 .31 - .47

MZMM .40 .28 - .50

DZFF .14 -.02 - .25

DZMM .20 .03 - .35

DZFM .15 .04 - .26 Gillespie et al. (2001)

DZcorr = ½ MZcorr A

DZcorr = MZcorr C

DZcorr < ½ MZcorr D

DZcorr = MZcorr = 0 E

Genetic analysis of the dimensions of Eysenck & CloningerN = 3127 Australian twins aged aged 18 -28 yrs

Neuroticism

A C E -2LL Df df -2LL p

.38 .00 .62 12949.85 3534

Gillespie et al. (2001)

P1

Twin1

e1

E1 A1 C1

a1 c1

P1

Twin1

e1

E1 A1

a1

Neuroticism

A C E -2LL Df df -2LL p

.38 .00 .62 12949.85 3534

.38 .62 12949.85 3535 1 1.11 .29

Gillespie et al. (2001)

P1

Twin1

e1

E1 C1

c1

Neuroticism

A C E -2LL Df df -2LL p

.38 .00 .62 12949.85 3534

.38 .62 12949.85 3535 1 1.11 .29

.27 .73 12971.50 3535 1 21.65 ***

Gillespie et al. (2001)

P1

Twin1

e1

E1

Neuroticism

A C E -2LL Df df -2LL p

.38 .00 .62 12949.85 3534

.38 .62 12949.85 3535 1 1.11 .29

.27 .73 12971.50 3535 1 21.65 ***

1.00 13066.63 3536 2 116.78 ***Gillespie et al. (2001)

P1

Twin1

e1

E1 A1

a1

Neuroticism

A C E -2LL Df df -2LL p

.38 .00 .62 12949.85 3534

.38 .62 12949.85 3535 1 1.11 .29

.27 .73 12971.50 3535 1 21.65 ***

1.00 13066.63 3536 2 116.78 ***Gillespie et al. (2001)

A C E

Neuroticism .38 - .62

Extraversion .46 - .54

Psychoticism .40 - .60

Lie .44 - .56

Harm Avoidance .44 - .56

Novelty Seeking .38 - .62

Reward Dependence .35 - .65

Gillespie et al. (2001)

Univariate heritability estimates for dimensions of Eysenck & CloningerN = 3127 Australian twins aged aged 18 -28 yrs

- Twin pairs reared together- Separated twin pairs- Non-twin adoptees & biological & adoptive families- Twin pairs reared together & their relatives

Personality modelsEysenck - Psychoticism - Extraversion

- Neuroticism - Lie

Cloninger - HA, NS, RD, PERS + 3 characters

Costa & McCrae - NEO-PI FFM

Application to personality researchHow compelling is evidence for a genetic contribution to adult and adolescent personality?

Genetic Variance

AdditiveEpistatic

(AA)

M F M F

Extraversion 10 13 37 39

Neuroticism 16 38 22 13

Sex differences & non-additivity in Neuroticism and Extraversion US, Australian & Finnish twins, their siblings, parents & spouses (N = 42,374) (Eaves et al., 1998)

Multivariate analysis of personality

Univariate analysis - estimates the contribution of A C & E within a dimension- says nothing about the underlying genetic & environmental causes of covariation between dimensions

Multivariate analysis - analyses cross-twin cross-trait correlations- determines the degree to which separate genetic & environmental factors are responsible for the correlations between variables

Multivariate genetic analyses

1. Cholesky Triangular Decomposition2. Common Pathway3. Independent Pathway

Multivariate Analysis

Var1 Var2 Var3

F

e e e

Multivariate Analysis

Var1 Var2 Var3

G

G G GE

E

E E

1. Cholesky Triangular Decomposition

Var1 Var2 Var3

A3A2A1

C3C2C1 E3E2E1

- Number of latent factors equals number of

variables

- Does not distinguish between common factor & specific factor effects except for final variable

- Analogous to Principle Components Analysis

Multivariate genetic analysis - Cholesky Decomposition

Cloninger’s 3 dimensions of character

- Self-directedness (DIRECT) - Cooperativeness (COOP)- Self-transcendence (TRANS)

Study of 3040 twins aged 50-96 years (Gillespie et al., submitted)

Univariate modeling

A C E

DIRECT .35 - .65

COOP .27 - .73

TRANS .44 - .56

Polychoric correlations

1. 2. 3.

1. DIRECT 1

2. COOP .17 1

3. TRANS -.04 .26 1

A1 A3

DIRECT COOP TRANS

A2

E1 E3E2C1 C3C2

Multivariate model fitting – DIRECT, COOP, TRANS

-2ll df -2ll df p AIC

ACE 26421.11 8166

A1 A3

DIRECT COOP TRANS

A2

E1 E3E2

Multivariate model fitting – DIRECT, COOP, TRANS

-2ll df -2ll df p AIC

ACE 26421.11 8166

AE 26421.13 8172 .02 6 1.00 -11.98

C1 C3

DIRECT COOP TRANS

C2

E1 E3E2

Multivariate model fitting – DIRECT, COOP, TRANS

-2ll df -2ll df p AIC

ACE 26421.11 8166

AE 26421.13 8172 .02 6 1.00 -11.98

CE 26447.01 8172 25.90 6 *** 13.90

DIRECT COOP TRANS

E1 E3E2

Multivariate model fitting – DIRECT, COOP, TRANS

-2ll df -2ll df p AIC

ACE 26421.11 8166

AE 26421.13 8172 .02 6 1.00 -11.98

CE 26447.01 8172 25.90 6 *** 13.90

E 26716.93 8178 295.82 12 *** 271.82

Best fitting multivariate model with standardized path coefficientsfrom latent additive genetic (A1 – A3) & non -shared environmental(E1 – E3) factors to the three dimensions of character

A1A3

DIRECT COOP TRANS

A2

E1 E3E2

.60

-.06

.16

-.46-.23

.62

.80

-.01

.10

.87 .73.19

2. Common Pathway 3. Independent Pathway

Latent Factor

A E

Var1 Var2 Var3

A EA EA E CC C

CA E

Var1 Var2 Var3

A EA EA E CC C

C

Multivariate genetic analysis - Common & Independent Pathway Models

- 3512 female twins aged 18-45 yrs

Measures of maternal / paternal behaviourFactor analysis of Parental Bonding Instrument (PBI) (Parker)

- Autonomy - Overprotection- Coldness

Measures of psychiatric distressFactor analysis of DSSI/sAD & SCL90 items

- Depression - Anxiety - Somatic Distress

Best fitting multivariate model for the three PBI dimensions: Coldness, Overprotecting & Autonomy

PARENTING

A E

Cold Overp Auto

EA EA E

C

.45 .27.28

.33.47.41.28.30

.35.82.65

Best fitting multivariate model for the dimensions of Depression, Anxiety & Somatic Distress

DISTRESS

A E

Depression Anxiety Somatic

EA EA E

.48 .52

.33.22.12.31.08

.82.69.78

A

.19

Important applications

- Recursive Models- Social Interaction- Modeling Direction of Causation

- Simplex modeling- Sex limitation- Genotype Environment Interactions- Genotype Environment Correlations- Assortment & Cultural Transmission

Important applications

- Recursive Models- Social Interaction- Modeling Direction of Causation

- Simplex modeling- Sex limitation- Genotype Environment Interactions- Genotype Environment Correlations- Assortment & Cultural Transmission

Recursive models: Direction of Causation

Under some circumstances we can model ‘direction of causation’ on cross sectional data

- Genetically informative data- Qualitatively or quantitatively distinct

Does parenting have an effect on psychological distress outcomes or vice a versa? Or is association between psychological distress and parenting determined by a common diathesis?

- 3500 female twins aged 18-45 years - questionnaire containing SCL90, DSSI & PBI

MZ=(a2+d2)iB

DZ=(½a2+¼d2)iB

aBdB aB

1

1MZ, ½DZ

1MZ, ¼DZ

dBeB eB

cA eAeA cA

iB iB

At1

Bt1

E A D

E C

At2

Bt2

D A E

C E

aBdB aB

1

1MZ, ½DZ

1MZ, ¼DZ

dBeB eB

cA eAeA cA

iA iA

At1

Bt1

E A D

E C

At2

Bt2

D A E

C E

MZ=c2iB

DZ=c2iB

A B B A

Direction of causation (DOC) modeling

C E C EC EAE EAEA

COLD AUTOOVERP

A E

DEP SOMANX

DISTRESS PARENTING

A EC

.63.67 .49 .56

.16 .52

.55 .20 .25.38 .45

.36 .13 .21 .11 .40 .17 .26 .21 .14 .49 .11 .37

Measuring stability of genetic effects over timeEaves, Eysenck & Martin (1989)

- Adult personality - High genetic continuity over time

- Effect stronger in Neuroticism vs Extraversion

Genetic continuity in adolescents?- Simplex modeling (Boomsma, 1989)

- Changes in the magnitude of G & E over time?- Do same G & E effects operate throughout time?

Genetic Simplex modeling

Autoregressive model

Structural equation model

ηi = βi ηi-1 + ζi

var (ηi) = βi2 var (ηi-1) + var (ζi)

ζ i-1

i-1

Yi-1Yi Yi+1

ζ i ζ i+1

λ i-1 λ i λ i+1ε i-1

β iβ i+1i i+1

ε i ε i+1

Measurement model

Yi = λi ηi + εi

var (Yi) = λi2 var (ηi) + var (εi)

Extraversion - Best fitting Simplex model

.23

.92

.53 .72

E12

A1

E1

E14

A2

E2

E16

A3

E3

.23 .23

.12.38

.93

.35 .18 .17

1 1 1

1 1 1

Neuroticism - Best fitting Simplex model

.22

.93

.49 .91

N12

A1

E1

N14

A2

E2

N16

A3

E3

.22 .22

.11.36 .11

.76

.39 .22 .15

1 1 1

1 1 1

Genes, personality & psychopathology

1. Dopamine receptor gene (DRD4)2. Monoamine oxidase (MAO) gene3. Quantitative trait loci (QTLs) for Neuroticism

Genes, personality & psychopathology

1. Dopamine receptor gene (DRD4)2. Monoamine oxidase (MAO) gene3. Quantitative trait loci (QTLs) for Neuroticism

3. Detecting QTLs for Anxiety & Depression

Difficult task - Polygenetic traits

Significant linkage between Harm Avoidance (HA) & locus on chromosone 8p21-23

- 38% of trait variance in HA- 758 sib-pairs- replication?

Need to screen ~20,000 sib pairs to identify QTL for 10% variance

NEU ANX DEP

A

a a a

35%35%

30%

16% 3% 6%

Neuroticism, Anxiety & Depression have been shown to be influenced by the same genes

Increasing power to detect QTLs

Zhang & Risch - Extreme Discordant & Concordant sib pair design (EDAC)

Majority of twin pairs provide little power to detect linkage

Only pairs that are concordant for high values, low values, or extremely discordant pairs (e.g. top & bottom 10%) provide substantial power

Anxiety Project

1988-1991 Twins and family members - Neuroticism data - scores fell into top two or bottom two deciles- 2918 individuals

1999 Anxiety Project- 2470 individuals - Blood & bucal samples

- DSM-IV & ICD-10 diagnoses

- Depression - Dysthymia - Social phobia

- Agoraphobia - GAD - Panic disorder

- OCD

NEURO

14.0012.0010.008.006.004.002.000.00

NEURO

Fre

qu

en

cy

2000

1000

0

Std. Dev = 3.41

Mean = 5.13

N = 9147.00149

581

973

1385

17841854

1718

703

Prevalences (%) - FemalesDeciles

Diagnosis1,2

(852)9,10(676) OR p

Depression 14.0 41.1 2.9 <.001

Dysthymia 0.2 1.9 9.5 <.001

OCD 1.4 9.2 6.4 <.001

Social Phobia 2.1 11.4 5.4 <.001

GAD 1.1 1.6 1.5 0.33

Panic w/o agoraphobia 1.3 4.1 3.2 <.001

Panic w agoraphobia 0.5 2.4 4.8 0.001

Agoraphobia w/o panic 0.7 4.1 5.9 <.001

QTL analysis of Neuroticism (Mailed questionnaires 1989 & 1999)

Chromosome ‘a’

0

5

10

15

20

25

Position in cM

Ch

isq

uar

e

N99 (mail)

N89 (mail)

chisq (joint - 2df)

C 21 = 23.82

http://ibgwww.colorado.edu/twins2002/

Important applications

- Recursive Models- Social Interaction- Modeling Direction of Causation

- Simplex modeling- Sex limitation- Genotype Environment Interactions- Genotype Environment Correlations- Assortment & Cultural Transmission

Recursive models: Social Interaction

Social / Sibling interaction - cooperative or negative effects

P1 = sP2 + aA1 + cC1 + eE1

P2 = sP1 + aA2 + cC2 + eE2

Little evidence- IQ- education- personality- social attitudes- BMI- heart rate etc

P1

e

E1 A1 C1

ac

1.0 MZ / DZ

1.0 MZ / 0.5 DZ

P2

c

C2 A2 E2

ae

s

s

Recursive models: Direction of Causation

Under some circumstances we can model ‘direction of causation’ on cross sectional data

Does parenting have an effect on psychological distress outcomes or vice a versa? Or is association between psychological distress and parenting determined by a common diathesis?

- 3500 female twins aged 18-45 years - questionnaire containing SCL90, DSSI & PBI

MZ=(a2+d2)iB

DZ=(½a2+¼d2)iB

aBdB aB

1

1MZ, ½DZ

1MZ, ¼DZ

dBeB eB

cA eAeA cA

iB iB

At1

Bt1

E A D

E C

At2

Bt2

D A E

C E

aBdB aB

1

1MZ, ½DZ

1MZ, ¼DZ

dBeB eB

cA eAeA cA

iA iA

At1

Bt1

E A D

E C

At2

Bt2

D A E

C E

MZ=c2iB

DZ=c2iB

A B B A

Direction of causation (DOC) modeling

C E C EC EAE EAEA

COLD AUTOOVERP

A E

DEP SOMANX

DISTRESS PARENTING

A EC

.63.67 .49 .56

.16 .52

.55 .20 .25.38 .45

.36 .13 .21 .11 .40 .17 .26 .21 .14 .49 .11 .37

Measuring stability of genetic effects over timeEaves, Eysenck & Martin (1989)

- Adult personality - High genetic continuity over time

- Effect stronger in Neuroticism vs Extraversion

Genetic continuity in adolescents?- Simplex modeling (Boomsma, 1989)

Genetic Simplex modeling

Autoregressive model

Structural equation model

ηi = βi ηi-1 + ζi

var (ηi) = βi2 var (ηi-1) + var (ζi)

ζ i-1

i-1

Yi-1Yi Yi+1

ζ i ζ i+1

λ i-1 λ i λ i+1ε i-1

β iβ i+1i i+1

ε i ε i+1

Measurement model

Yi = λi ηi + εi

var (Yi) = λi2 var (ηi) + var (εi)

Extraversion - Best fitting Simplex model

ε1.23

.92

.53 .72

E12

A1

E1

E14

A2

E2

E16

A3

E3

ε1.23

ε1.23

ζA2

.12ζA1

.38

.93

ζE1

.35

ζE2

.18

ζE3

.17

1 1 1

1 1 1

Neuroticism - Best fitting Simplex model

ε1.22

.93

.49 .91

N12

A1

E1

N14

A2

E2

N16

A3

E3

ε1.22

ε1.22

ζA2

.11ζA1

.36ζA3

.11

.76

ζE1

.39

ζE2

.22

ζE3

.15

1 1 1

1 1 1

Sex limitation

Is the magnitude of A, C & E effects equivalent across sex? Sex specific genes?

Requires data from opposite-sex DZ twins

1. General model for sex limitation2. Common effects sex limitation3. Scalar sex limitation

Sex limitation1. General model for sex limitation

ef

Ef Af

af

AmEm

ama’m

A’m

em

Pf Pm

Sex limitation1. General model for sex limitation2. Common effects sex limitation

ef

Ef Af

af

AmEm

amem

Pf Pm

Sex limitation1. General model for sex limitation2. Common effects sex limitation3. Scalar sex limitation

Body Mass Index (BMI)Personality

L1f

e

Ef Af

a

L1m

AmEm

ae

L2f L2m

1.0

1.0

k

k

Pf Pm

Genotype Environment InteractionGenetic control of sensitivity to differences in the environment. Components of phenotypic variance conditional on environmental exposure

- 5967 twin pairs aged 18 to 88 yrs- Genetic heritability of depression differs with marital status: Singles = 42% vs Married = 30%

Genotype Environment CorrelationEnvironments which individuals experience may not be random but may be caused by or correlated with their genes (CorGE)

- depression & adverse environments- IQ & education / enriched environment

Detecting GE and corrGE- Requires large sample sizes + data from twin & relatives

Assortment & Cultural TransmissionAssortative mating ‘like marrying like’

- based on social background / relatives (C) - Social homogamy

- based on phenotype of spouse (ACE)- Phenotypic assortment

Cultural transmission- environmental influences in present generation stem from parental phenotypes

Assortment & Cultural Transmission

Pm

g

Gm

e

Em

Child 1

g

Gm

e

Em

Pf

e g

e g

Ef Gf

u

b bbb

1/21/2

Child 2

Ef Gf

AT2CTAT1

PT2

ET2a e

PT1

eET1 a

c c

rAT1 rAT1

1/2 1/2

CM

PM

e

EM AM

ac

PM

EFAFCF

a ec

Cultural transmission & social homogamy

m f

rCT

Does ‘like marry like’? Marital correlations for personality & social attitudes

Psychoticism = 0.16 Religion = 0.52Extraversion = 0.06 Socialism = 0.54Neuroticism = 0.13 Prejudice = 0.35Lie = 0.28 Permissiveness = 0.52

Authoritarianism = 0.56

Distinguishing environmental & biological transmission

- little evidence for environmental transmission

Neuroticism - Lake et al. (1999)- 45 850 twins & relatives- modest assortative mating, A, D & E- no environmental transmission - Neuroticism little more complex than found

from earlier, simpler designs

Dopamine receptor gene (DRD2) genotype & heroin addiction: A1 allele frequency

05

1015202530354045

Succes

s

Dropout

Poor

f(A1)

- 95 Brisbane heroin addicts on methadone treatment program

- 54 successful treatment outcome

- 22 dropped out

- 19 poor treatment outcome

Lawford et al (2000) Am J Med Genet 96:592-8